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Journal of Computational Neuroscience

, Volume 45, Issue 3, pp 173–191 | Cite as

Linear-nonlinear-time-warp-poisson models of neural activity

  • Patrick N. LawlorEmail author
  • Matthew G. Perich
  • Lee E. Miller
  • Konrad P. Kording
Article

Abstract

Prominent models of spike trains assume only one source of variability – stochastic (Poisson) spiking – when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When applied to recordings from macaque premotor cortex, we find that time warping considerably improves predictions of neural activity. We suggest that such temporal variability is a widespread phenomenon in the brain which should be modeled.

Keywords

Modeling Spike trains Poisson process Generalized linear model Reaching movements 

Notes

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

Supplementary material

10827_2018_696_MOESM1_ESM.pdf (1.2 mb)
(PDF 1.20 MB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Child NeurologyChildren’s Hospital of PhiladelphiaPhiladelphiaUSA
  2. 2.University of GenevaGenevaSwitzerland
  3. 3.Department of PhysiologyNorthwestern UniversityChicagoUSA
  4. 4.Departments of Bioengineering and NeuroscienceUniversity of PennsylvaniaPhiladelphiaUSA

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